Monitoring of manufacturing equipment is achieved by recording data from the manufacturing equipment (e.g., tool or chamber) during the performance of manufacturing tasks. That is, monitoring of the tool is achieved during normal operating conditions. Data may also be recorded while the tool is performing other routine tasks such as conditioning steps (cleaning, warm-up, seasoning, etc.) that prepare the tool for the next manufacturing process step. The data consists of recorded values from multiple elements on the tool collectively known as sensors. Examples of sensors include: temperature, flow rate, pressure, power as well as control elements such as pressure control valves and variable capacitors.
The data are used to monitor the behavior of the tool during all of these tasks. The data may be used for statistical process control (SPC), Fault Detection and Classification (FDC) or other monitoring or analyses. The methods for observing tool behavior may include univariate or multivariate methods for determining unusual behavior such as drifts, shifts, instabilities or periodic behavior.
The data recorded from the tools represent a measure of the state of the tool at various times during the manufacturing process. However, due to physical and economical limitations as well as (in some cases) a simple lack of sensors, it represents an incomplete measure of the tool state.
While it is physically impossible to know everything about the state of a tool during manufacturing or running other routine tasks, it is certainly possible to add more sensors to the equipment. However, this may be difficult from both an engineering and economical perspective. Therefore, current tool monitoring methods are inherently incomplete.
More specifically, as some aspects of the process are invisible either because the sensor does not exist or because a sensor is not sensitive to possible variations, it is not possible to make accurate measurements of important tool and process properties. For example, some gas properties in plasma processing are not observable with existing chamber sensors, e.g., gas temperature and gas dissociation level. Also, some chamber properties are not observable during process, e.g., chamber wall condition and outgassing, chamber conductance, and pressure or mass flow control (MFC) calibration.
Accordingly, there exists a need in the art to overcome the deficiencies and limitations described hereinabove.